Crowd trajectory prediction method and system in shopping center traffic space based on artificial jellyfish search algorithm

Abstract

This invention proposes a method and system for predicting crowd trajectories in shopping mall traffic spaces based on an artificial jellyfish search algorithm. The method comprises the following steps: Step 1: Collecting and correlating shopping mall scene parameter information and crowd behavior data; Step 2: Determining decision variables, including the position coordinates and velocity components of individual crowd members, and using channel attraction parameters, node turning probability parameters, stair / escalator / elevator selection probability parameters, and speed adjustment parameters as factors influencing current direction; Step 3: Initializing a jellyfish population, treating individual crowd members as jellyfish, setting the population size and initializing decision variables according to specific rules; and Step 4: Iterating the artificial jellyfish search algorithm to obtain the final crowd trajectory prediction. This method achieves accurate prediction of crowd trajectories in shopping mall traffic spaces.

Description

Technical Field

[0001] The present invention belongs to the technical field of crowd trajectory analysis and prediction, and in particular relates to a method and system for predicting crowd trajectories in a shopping center traffic space based on an artificial jellyfish search algorithm. Background Art

[0002] In the operation and management of modern shopping malls, accurately predicting crowd movements is crucial for improving operational efficiency, optimizing the customer experience, and ensuring safety. As shopping malls continue to expand in scale and become increasingly complex in their functions, crowd movement within them is influenced by the interaction of multiple factors. Among these factors, the layout of traffic spaces (including aisles, nodes, stairways, elevators, their geometric features, and signage) plays a key role in guiding crowd movement and behavior.

[0003] Traditional trajectory prediction methods have too many limitations. This simulation based on natural biological behavior enables the algorithm to search and optimize in a way that is more in line with natural laws when solving certain complex problems, providing new ideas and methods for solving problems. While achieving the best prediction results, it can also reduce production costs.

[0004] The Artificial Jellyfish Search (JS) optimizer, proposed by Zhou Ruisheng in 2020, is based on a novel optimization algorithm. It boasts strong optimization capabilities and rapid convergence. Its principle simulates the search behavior of jellyfish, including their following ocean currents, their movement within swarms (both active and passive), the timing control mechanism for switching between these movements, and their aggregation into jellyfish clusters. The algorithm makes two assumptions: jellyfish either follow ocean currents or move within swarms, and a "timing control mechanism" governs the transition between these types of movement. Jellyfish migrate through the ocean in search of food and are more likely to be attracted to areas with greater food supplies. In trajectory prediction, the layout of traffic spaces plays a key role in guiding crowd flow and behavior. Existing crowd trajectory prediction methods lack effective solutions for accurately reflecting the flexibility and adaptability of crowd trajectories guided by traffic spaces. Summary of the Invention

[0005] This invention aims to address existing technical issues and meet practical needs. It proposes a method and system for predicting crowd trajectories in shopping mall traffic spaces based on an artificial jellyfish search algorithm. This algorithm accurately reflects the influence of traffic layout on crowd behavior, improving prediction efficiency.

[0006] The present invention is implemented through the following technical solutions. The present invention proposes a method for predicting crowd trajectories in a shopping center traffic space based on an artificial jellyfish search algorithm. The method comprises the following steps:

[0007] Step 1: Collect shopping mall scene parameter information and crowd behavior data, establish a dedicated data fusion platform, and associate shopping mall scene parameter information and crowd behavior data so that the crowd behavior data can be mapped to specific scene parameters;

[0008] Step 2: Treat the individuals in the crowd as jellyfish individuals, determine the position coordinates (x, y) as the basic decision variables, and the individual's horizontal and vertical velocity components are (v x ,v y ), the initial value of the speed is determined according to the statistical average of the collected crowd behavior data; other decision variables are defined as channel attraction parameters Node turning probability parameter p jk , Stairs, escalators, elevators selection parameters p s 、p l and p m and the speed adjustment parameter v adjust ;

[0009] Step 3: Initialize the jellyfish population and determine the population size N; the initial position coordinates (x, y) of the jellyfish individuals are randomly generated within the shopping mall, and the velocity component (v x ,v y ) is randomly initialized according to the speed range of the collected data, and other decision variables are initialized within the value range;

[0010] Step 4: Use the artificial jellyfish search algorithm to iterate the algorithm to obtain the final crowd trajectory prediction;

[0011] The prediction trajectory is specifically as follows: various factors that guide the flow of people in the shopping center traffic space are regarded as ocean currents, and the ocean current direction vectors of channel i, node j, stair s, escalator l and straight ladder m are calculated respectively; for each jellyfish individual n, the attraction of the ocean current to individual n is calculated. and the repulsive force between individual jellyfish The time step Δt is determined based on the characteristics of the data acquisition device. The speed and position of each individual in the crowd are updated based on the calculated attraction, repulsion, and inertia weights to obtain new speed and position coordinates, thereby simulating the movement trajectory of the crowd in the traffic space.

[0012] When updating positions, it is necessary to check whether individual crowd members exceed the boundaries of the shopping center. That is, the channel attraction parameters, node turning probability parameters, and stair, escalator, and elevator selection parameters are all within the interval [0,1]. If they exceed the boundaries, the parameters need to be adjusted back to a reasonable range to ensure that the predicted trajectory meets the actual spatial constraints.

[0013] The iteration termination condition is set, that is, the position change of all jellyfish individuals within M consecutive iterations is less than a certain threshold ξ. When the termination condition is met, the iteration is stopped and the output is the jellyfish trajectory in continuous time, that is, the predicted crowd trajectory.

[0014] Furthermore, a data fusion platform that links shopping mall scene parameter information and crowd behavior data is established. The data fusion platform adopts a three-layer architecture, including a data collection layer, an edge computing layer, and a cloud-based fusion analysis layer.

[0015] Among them, the data collection layer is composed of Wi-Fi positioning devices and smart cameras, which are responsible for collecting scene parameter information and crowd behavior data of the shopping mall; the edge computing layer is distributed in various areas of the shopping mall, close to the data collection equipment, and is composed of multiple edge computing nodes. These nodes can perform real-time pre-processing and preliminary analysis of the collected data, filter out redundant information to obtain the required traffic space information and crowd behavior information; the cloud fusion analysis layer is located in the data center, which is used to deeply integrate, analyze and store data from the edge computing layer.

[0016] Furthermore, the channel attraction parameter The node's turning probability parameter p jk , the selection probability parameter p of stairs, escalator, and elevator s 、p l and p m The value range of is [0-1], and each parameter is affected by different factors;

[0017] Among them, the channel attraction parameter of each channel i is The factors affected: channel width i , signage i Influence, the weights of each factor are α and β respectively, and α+β=1, calculate Among them, max(widht) and max(signage) are the maximum values ​​of all channel widths and signage perfection respectively;

[0018] The turning probability parameter p for each possible turning direction k of each node j jk ,

[0019] For each stair s, escalator l and elevator m, select the probability parameter ps 、p l and p m , are all affected by the convenience of location s / l / m Attraction of surrounding shops s / l / m Influence.

[0020] Furthermore, various factors that guide the flow of people in the shopping center's traffic space are regarded as ocean currents. Depending on the influencing factors, the ocean currents have different direction vectors;

[0021] Among them, for channel i, the vector corresponding to its width and identification factor And the corresponding weight coefficients α, β, the channel's ocean current direction vector is

[0022] For node j, according to the turning probability parameter p of each turning direction k, jk , and the environment vector associated with each steering direction The ocean current direction vector of node j is Turn probability parameter p jk As a weight, it reflects the likelihood of different turning directions in the crowd's choice;

[0023] For stairs s, escalators l and elevators m, the vectors corresponding to their locations and surrounding store attractiveness factors are: And the weight coefficients δ and ε, where δ + ε = 1, calculate the ocean current direction vector

[0024] Furthermore, each jellyfish individual is attracted by Includes channel attraction Node attractiveness Stairs / elevator or escalator / staircase attractiveness Target attractiveness

[0025] Among them, the channel attraction is: Among them, k1 is the channel attraction coefficient, is the channel attraction weight parameter of the channel where the individual is located, is the channel’s current direction vector;

[0026] The node attraction is: Among them, k2 is the node attraction coefficient, j represents the nodes around individual n, and p nj is the probability that individual n is affected by node j, is the current direction vector of the node;

[0027] Stairs / elevators or escalators / staircases have the following attractions: Where k3 is the attraction coefficient of stairs / elevators or escalators / elevators, s represents the stairs / elevators or escalators / elevators around individual n, is the probability that an individual chooses stairs / elevator or escalator / elevator, is the current direction vector of the stairs / elevator or escalator / staircase;

[0028] The target attraction is: Among them, k4 is the target attraction coefficient, is the vector pointing from the individual position to the target position, is the distance from the individual to the target location;

[0029] The total attraction is:

[0030] Furthermore, for any two jellyfish individuals n and q, the repulsive force between the jellyfish individuals is For any two jellyfish individuals n and q, the repulsive force is proportional to the distance d between them. nq Inversely proportional, the repulsive force factor is r, then in is the vector pointing from individual n to individual q, and all the repulsive forces interacting with individual n are superimposed, and the total repulsive force is

[0031] Furthermore, during the prediction process, the speed and movement position of the jellyfish in continuous time are updated based on the time step Δt;

[0032] The speed update formula is: in, is the new velocity of individual n at time t+Δt, is the old velocity of individual n at time t, w is the inertia weight, a is the attraction factor, r is the repulsion factor, is the attraction experienced by individual n at time t, is the repulsive force experienced by individual n at time t;

[0033] The position update formula is: in, is the new position of individual n at time t+Δt, is the old position of individual n at time t.

[0034] The present invention also proposes a shopping center traffic space crowd trajectory prediction system based on an artificial jellyfish search algorithm, the system comprising:

[0035] Data collection module: collects shopping mall scene parameter information and crowd behavior data, establishes a dedicated data fusion platform, and associates shopping mall scene parameter information and crowd behavior data so that crowd behavior data can be mapped to specific scene parameters;

[0036] Decision variable setting module: The individuals in the crowd are regarded as jellyfish individuals, and the position coordinates (x, y) are determined as the basic decision variables. The individual's velocity components in the horizontal and vertical directions are (v x ,v y ), the initial value of the speed is determined according to the statistical average of the collected crowd behavior data; other decision variables are defined as channel attraction parameters Node turning probability parameter p jk , Stairs, escalators, elevators selection parameters p s 、p l and p m and the speed adjustment parameter v adjust ;

[0037] Initialization module: Initialize the jellyfish population and determine the population size N; the initialization position coordinates (x, y) of the jellyfish individuals are randomly generated within the shopping mall, and the velocity component (v x ,v y ) is randomly initialized according to the speed range of the collected data, and other decision variables are initialized within the value range;

[0038] Trajectory prediction module: uses the artificial jellyfish search algorithm to perform algorithm iteration to obtain the final crowd trajectory prediction;

[0039] The prediction trajectory is specifically as follows: various factors that guide the flow of people in the shopping center traffic space are regarded as ocean currents, and the ocean current direction vectors of channel i, node j, stair s, escalator l and straight ladder m are calculated respectively; for each jellyfish individual n, the attraction of the ocean current to individual n is calculated. and the repulsive force between individual jellyfish The time step Δt is determined based on the characteristics of the data acquisition device. The speed and position of each individual in the crowd are updated based on the calculated attraction, repulsion, and inertia weights to obtain new speed and position coordinates, thereby simulating the movement trajectory of the crowd in the traffic space.

[0040] When updating positions, it is necessary to check whether individual crowd members exceed the boundaries of the shopping center. That is, the channel attraction parameters, node turning probability parameters, and stair, escalator, and elevator selection parameters are all within the interval [0,1]. If they exceed the boundaries, the parameters need to be adjusted back to a reasonable range to ensure that the predicted trajectory meets the actual spatial constraints.

[0041] The iteration termination condition is set, that is, the position change of all jellyfish individuals within M consecutive iterations is less than a certain threshold ξ. When the termination condition is met, the iteration is stopped and the output is the jellyfish trajectory in continuous time, that is, the predicted crowd trajectory.

[0042] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for predicting crowd trajectories in the traffic space of a shopping mall based on the artificial jellyfish search algorithm.

[0043] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for predicting crowd trajectories in a shopping mall traffic space based on an artificial jellyfish search algorithm.

[0044] Beneficial effects of the present invention:

[0045] This invention combines an innovative data fusion platform with an artificial jellyfish search algorithm to accurately predict crowd trajectories within shopping mall traffic spaces. The data fusion platform effectively integrates multi-source data, improving data quality and usability. The artificial jellyfish search algorithm fully considers traffic guidance and interactions between individuals, accurately simulating changes in crowd behavior. This provides a basis for decision-making in shopping mall operations management, such as optimizing store layouts, adjusting traffic infrastructure operation strategies, and developing targeted marketing plans. This improves operational efficiency, enhances the customer experience, and ensures safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0047] Figure 1 This is a flow chart of the method for predicting crowd trajectories in a shopping center traffic space based on the artificial jellyfish search algorithm of the present invention;

[0048] Figure 2 This is a block diagram of the shopping center traffic space crowd trajectory prediction system based on the artificial jellyfish search algorithm described in the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] The present invention proposes a method and system for predicting crowd trajectories in a shopping mall traffic space based on an artificial jellyfish search algorithm. The method comprises the following steps: Step 1: Collecting shopping mall scene parameter information and crowd behavior data, and associating the shopping mall scene parameter information and crowd behavior data; Step 2: Determining decision variables, including position coordinates and velocity components of individual crowd members, and using channel attraction parameters, node turning probability parameters, stair / escalator / elevator selection probability parameters, and speed adjustment parameters as factors affecting the direction of ocean currents; Step 3: Initializing a jellyfish population, using individual crowd members as jellyfish individuals, setting the population size and initializing decision variables according to certain rules; Step 4: Using factors guiding crowd flow in the shopping mall traffic space as ocean currents, calculating the ocean current direction vector and the attraction and repulsion forces on each jellyfish individual, updating the individual's velocity and position based on the inertia weight, attraction factor, and repulsion factor, checking boundary conditions, and stopping iteration when the position change of all jellyfish individuals within M consecutive iterations is less than a certain threshold ξ, thereby ultimately obtaining a predicted trajectory generated using the artificial jellyfish search algorithm.

[0051] Specifically, combined Figure 1-Figure 2 The present invention proposes a method for predicting crowd trajectories in a shopping center traffic space based on an artificial jellyfish search algorithm, the method comprising the following steps:

[0052] Step 1: Collect shopping mall scene parameter information and crowd behavior data, establish a dedicated data fusion platform, and associate the shopping mall scene parameter information and crowd behavior data so that the crowd behavior data can correspond to specific scene parameters; the shopping mall scene parameter information is the traffic space information within the building, including channel information, stairs, escalators and elevators information, as well as node information; the crowd behavior information includes pedestrian behavior information and trajectory feature data.

[0053] Step 2: Treat the individuals in the crowd as jellyfish individuals, determine the position coordinates (x, y) as the basic decision variables, and the individual's horizontal and vertical velocity components are (v x ,v y ), the initial value of the speed is determined according to the statistical average of the collected crowd behavior data; other decision variables are defined as channel attraction parameters Node turning probability parameter p jk , Stairs, escalators, elevators selection parameters p s 、pl and p m and the speed adjustment parameter v adjust ;

[0054] Step 3: Initialize the jellyfish population and determine the population size N; the initial position coordinates (x, y) of the jellyfish individuals are randomly generated within the shopping mall, and the velocity component (v x ,v y ) is randomly initialized according to the speed range of the collected data, and other decision variables are initialized within the value range;

[0055] Step 4: Use the artificial jellyfish search algorithm to iterate the algorithm to obtain the final crowd trajectory prediction;

[0056] The prediction trajectory is specifically as follows: taking various factors that guide the flow of people in the shopping center's traffic space as ocean currents, the ocean current direction vectors of channel i, node j, stair s, escalator l, and elevator m are calculated respectively; for each jellyfish individual n, considering the guiding force of the traffic space on the flow of people and the mutual repulsion between the individuals, and at the same time, in order to avoid excessive aggregation, the magnitude of the repulsive force is inversely proportional to the distance between the individuals, so the attraction of the ocean current to the individual n is calculated. and the repulsive force between individual jellyfish The time step Δt is determined based on the characteristics of the data acquisition device. The speed and position of each person in the crowd are updated based on the calculated attraction, repulsion, and inertia weights. The speed update formula comprehensively considers the influence of the previous speed, attraction, and repulsion, allowing each person in the crowd to gradually adjust their direction and speed during movement. The position update is calculated based on the updated speed to obtain the new speed and position coordinates, thereby simulating the movement trajectory of the crowd in the traffic space.

[0057] When updating positions, it is necessary to check whether individual crowd members exceed the boundaries of the shopping center. That is, the channel attraction parameters, node turning probability parameters, and stair, escalator, and elevator selection parameters are all within the interval [0,1]. If they exceed the boundaries, the parameters need to be adjusted back to a reasonable range to ensure that the predicted trajectory meets the actual spatial constraints.

[0058] The iteration termination condition is set, that is, the position change of all jellyfish individuals within M consecutive iterations is less than a certain threshold ξ. When the termination condition is met, the iteration is stopped and the output is the jellyfish trajectory in continuous time, that is, the predicted crowd trajectory.

[0059] Establish a data fusion platform that links shopping mall scene parameter information and crowd behavior data. The data fusion platform adopts a three-layer architecture, including data collection layer, edge computing layer and cloud fusion analysis layer;

[0060] Among them, the data collection layer is composed of Wi-Fi positioning devices and smart cameras, which are responsible for collecting scene parameter information and crowd behavior data of the shopping mall; the edge computing layer is distributed in various areas of the shopping mall, close to the data collection equipment, and is composed of multiple edge computing nodes. These nodes can perform real-time pre-processing and preliminary analysis of the collected data, filter out redundant information to obtain the required traffic space information and crowd behavior information; the cloud fusion analysis layer is located in the data center, which is used to deeply integrate, analyze and store data from the edge computing layer.

[0061] Edge computing nodes and data collection devices are connected via a wireless network, ensuring fast and stable data transmission to the edge computing layer. Edge computing nodes also share and collaborate on data through an internal network. Nodes in different regions can collaborate to analyze cross-regional crowd behavior. The edge computing layer communicates with the cloud-based fusion analysis layer via a high-speed broadband network, uploading processed data to the cloud.

[0062] A Bayesian network model is constructed to integrate traffic space information and human trajectory data. The status (unblocked, congested, under maintenance, etc.) and attributes (such as width, length, and carrying capacity) of traffic space elements (channels, nodes, stairs, elevators, and escalators) are used as nodes, and pedestrian behavior (walking, stopping, path selection, etc.) and trajectory characteristics (speed, direction, dwell time, etc.) are used as other nodes. The conditional probability relationship between nodes is determined based on prior knowledge and statistical data analysis. For example, if a channel is known to be narrow and frequently congested, the probability of pedestrians choosing that channel may be low, while the probability of choosing alternative paths (such as adjacent wider channels) may increase. This relationship can be represented using a conditional probability table.

[0063] Shopping mall scene parameter information includes: channel information; information about stairs, escalators, and elevators; and node information. Crowd behavior information includes pedestrian behavior information and trajectory feature data. Channel information: Obtain width data for all channels within the shopping mall and identification information within the channels; Stair, escalator, and elevator information: For stairs, collect information such as their location, number, stairwell width, and surrounding space layout; For escalators and elevators, record their location, carrying capacity, speed, stop floors, entrance and exit locations, and surrounding space layout data; Node information: Determine the location and attributes of nodes such as intersections, junctions, and branch points between channels, including the number of channels connected to the nodes, the types of stores around the nodes, and rest facilities. Pedestrian behavior information: Walking, staying, and choosing a path; Trajectory feature data: Speed, direction, and dwell time.

[0064] Channel attraction parameter The node's turning probability parameter p jk , the selection probability parameter p of stairs, escalator, and elevators 、p l and p m The value range of is [0-1], and each parameter is affected by different factors;

[0065] Among them, the channel attraction parameter of each channel i is The factors affected: channel width i , signage i Influence, the weights of each factor are α and β respectively, and α+β=1, calculate Among them, max(width) and max(signage) are the maximum values ​​of all channel widths and signage perfection, respectively;

[0066] The turning probability parameter p for each possible turning direction k of each node j jk ,

[0067] For each stair s, escalator l and elevator m, select the probability parameter p s 、p l and p m , are all affected by the convenience of location s / l / m Attraction of surrounding shops s / l / m Influence.

[0068] The various factors that guide the flow of people in the shopping center traffic space are regarded as ocean currents. Depending on the influencing factors, the ocean currents have different direction vectors.

[0069] Among them, for channel i, the vector corresponding to its width and identification factor And the corresponding weight coefficients α, β, the channel's ocean current direction vector is

[0070] For node j, according to the turning probability parameter p of each turning direction k, jk , and the environment vector associated with each steering direction This vector comprehensively considers factors such as the width of the turning direction connecting channel, signs, and the attractiveness of surrounding shops. The current direction vector of node j is Turn probability parameter p jk As a weight, it reflects the likelihood of different turning directions in the crowd's choice;

[0071] For stairs s, escalators l and elevators m, the vectors corresponding to their locations and surrounding store attractiveness factors are: And the weight coefficients δ and ε, where δ + ε = 1, calculate the ocean current direction vector

[0072] Each jellyfish individual is attracted by Includes channel attraction Node attractiveness Stairs / elevator or escalator / staircase attractiveness Target attractiveness

[0073] Among them, the channel attraction is: Among them, k1 is the channel attraction coefficient, is the channel attraction weight parameter of the channel where the individual is located, is the channel’s current direction vector;

[0074] The node attraction is: Among them, k2 is the node attraction coefficient, j represents the nodes around individual n, and p nj is the probability that individual n is affected by node j, is the current direction vector of the node;

[0075] Stairs / elevators or escalators / staircases have the following attractions: Where k3 is the attraction coefficient of stairs / elevators or escalators / elevators, s represents the stairs / elevators or escalators / elevators around individual n, is the probability that an individual chooses stairs / elevator or escalator / elevator, is the current direction vector of the stairs / elevator or escalator / staircase;

[0076] The target attraction is: Among them, k4 is the target attraction coefficient, is the vector pointing from the individual position to the target position, is the distance from the individual to the target location;

[0077] The total attraction is:

[0078] For any two jellyfish individuals n and q, the repulsive force between the jellyfish individuals is For any two jellyfish individuals n and q, the repulsive force is proportional to the distance d between them. nq Inversely proportional, the repulsive force factor is r, then in is the vector pointing from individual n to individual q, and all the repulsive forces interacting with individual n are superimposed, and the total repulsive force is

[0079] During the prediction process, the speed and movement position of the jellyfish in continuous time are updated based on the time step Δt;

[0080] The speed update formula is: in, is the new velocity of individual n at time t+Δt, is the old velocity of individual n at time t, w is the inertia weight, a is the attraction factor, r is the repulsion factor, is the attraction experienced by individual n at time t, is the repulsive force experienced by individual n at time t;

[0081] The position update formula is: in, is the new position of individual n at time t+Δt, is the old position of individual n at time t.

[0082] Using the fitness function, F(v i )=w1E distance +w2E node +w3E flow .

[0083] Among them, w1, w2, w3 are weight coefficients, E distance is the trajectory distance error, E node is the node passing error, E flow The flow error measures how closely the predicted trajectories match actual crowd behavior. Trajectory distance error is calculated by calculating the average Euclidean distance between the predicted and actual trajectory points. Node passage error measures the difference between the predicted time it takes for people to pass through key nodes and the actual time it takes them to pass through. The flow error compares the difference between the predicted and actual flow rates for different elements of the traffic space.

[0084] The algorithm terminates when the maximum number of iterations is reached or when an individual with a fitness value less than a pre-set precision threshold appears in the population. The number of consecutive iterations M in the iterative termination condition ranges from [50, 200], and the precision threshold ξ ranges from [0.01, 0.1]. The specific value can be adjusted and determined based on factors such as the size of the shopping mall and the complexity of crowd behavior. For large shopping malls with complex and changeable crowd behavior, the M value can be appropriately increased to between 150 and 200, while the ξ value can be reduced to between 0.01 and 0.05 to ensure the accuracy of the prediction results. For small shopping malls or shopping malls with relatively simple crowd behavior, the M value is between 50 and 100, and the ξ value is between 0.05 and 0.1, which can improve computational efficiency while ensuring a certain level of prediction accuracy.

[0085] The present invention also proposes a shopping center traffic space crowd trajectory prediction system based on an artificial jellyfish search algorithm, the system comprising:

[0086] Data collection module: collects shopping mall scene parameter information and crowd behavior data, establishes a special data fusion platform, and associates shopping mall scene parameter information and crowd behavior data so that the crowd behavior data can correspond to specific scene parameters; the shopping mall scene parameter information is the traffic space information within the building, including channel information, stairs, escalators and elevators information and node information; the crowd behavior information includes pedestrian behavior information and trajectory feature data.

[0087] Decision variable setting module: The individuals in the crowd are regarded as jellyfish individuals, and the position coordinates (x, y) are determined as the basic decision variables. The individual's velocity components in the horizontal and vertical directions are (v x ,v y ), the initial value of the speed is determined according to the statistical average of the collected crowd behavior data; other decision variables are defined as channel attraction parameters Node turning probability parameter p jk , Stairs, escalators, elevators selection parameters p s 、p l and p m and the speed adjustment parameter v adjust ;

[0088] Initialization module: Initialize the jellyfish population and determine the population size N; the initialization position coordinates (x, y) of the jellyfish individuals are randomly generated within the shopping mall, and the velocity component (v x ,v y ) is randomly initialized according to the speed range of the collected data, and other decision variables are initialized within the value range;

[0089] Trajectory prediction module: uses the artificial jellyfish search algorithm to perform algorithm iteration to obtain the final crowd trajectory prediction;

[0090] The prediction trajectory is specifically as follows: taking various factors that guide the flow of people in the shopping center's traffic space as ocean currents, the ocean current direction vectors of channel i, node j, stair s, escalator l, and elevator m are calculated respectively; for each jellyfish individual n, considering the guiding force of the traffic space on the flow of people and the mutual repulsion between the individuals, and at the same time, in order to avoid excessive aggregation, the magnitude of the repulsive force is inversely proportional to the distance between the individuals, so the attraction of the ocean current to the individual n is calculated. and the repulsive force between individual jellyfish The time step Δt is determined based on the characteristics of the data acquisition device. The speed and position of each person in the crowd are updated based on the calculated attraction, repulsion, and inertia weights. The speed update formula comprehensively considers the influence of the previous speed, attraction, and repulsion, allowing each person in the crowd to gradually adjust their direction and speed during movement. The position update is calculated based on the updated speed to obtain the new speed and position coordinates, thereby simulating the movement trajectory of the crowd in the traffic space.

[0091] When updating positions, it is necessary to check whether individual crowd members exceed the boundaries of the shopping center. That is, the channel attraction parameters, node turning probability parameters, and stair, escalator, and elevator selection parameters are all within the interval [0,1]. If they exceed the boundaries, the parameters need to be adjusted back to a reasonable range to ensure that the predicted trajectory meets the actual spatial constraints.

[0092] The iteration termination condition is set, that is, the position change of all jellyfish individuals within M consecutive iterations is less than a certain threshold ξ. When the termination condition is met, the iteration is stopped and the output is the jellyfish trajectory in continuous time, that is, the predicted crowd trajectory.

[0093] Example

[0094] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0095] The Artificial Jellyfish Search (JS) optimizer, proposed by Zhou Ruisheng in 2020, is a novel optimization algorithm. It boasts strong optimization capabilities and rapid convergence. Its principle simulates the searching behavior of jellyfish, including their movement within a swarm (both active and passive) following ocean currents, the timing of these movements, and the process of their aggregation into clusters.

[0096] Combine Figure 1-2 The present invention proposes a method for predicting crowd trajectories in a shopping center traffic space based on an artificial jellyfish search algorithm, the method comprising:

[0097] Step 1: Collect data and establish a dedicated data fusion platform to correlate shopping center scene parameter information with crowd behavior data so that crowd behavior data can be mapped to specific scene parameters;

[0098] Consider a large shopping mall, covering an area of ​​100,000 square meters, with a multi-story building structure, including numerous shops, restaurants, entertainment facilities, etc. During peak hours, the flow of people is large and the crowd behavior is complex and diverse.

[0099] Through the data collection layer of the data fusion platform, Wi-Fi positioning devices and smart cameras provide comprehensive coverage throughout the shopping mall. Wi-Fi positioning devices monitor signals from connected customer mobile devices in real time, acquiring location information with an accuracy of 3-5 meters. Smart cameras capture images at a 25-frame-per-second rate, covering key locations such as aisles, stairways, elevators, store entrances, and rest areas.

[0100] The collected scene parameter information includes: channel widths ranging from 2 to 5 meters, signage information including direction signs and store signs, and the completeness of signage varies in different areas;

[0101] The staircases and elevators are reasonably distributed, with a stairwell width of approximately 2-3 meters. The elevators have a carrying capacity of 10-15 people and a moderate speed. They stop at all floors. The entrances and exits are coordinated with the surrounding passages and store layout, and the surrounding space layout takes into account the flow of people and evacuation needs.

[0102] The locations of nodes (channel intersections, confluence points, branch points) are clear, the number of connecting channels ranges from 2 to 4, the types of shops around the nodes are rich and diverse, and the rest facilities are reasonably configured.

[0103] In terms of crowd behavior data, pedestrians’ walking paths, stop locations and times, path selection conditions, and trajectory feature data with speeds between 0.5-1.5 m / s, frequent direction changes, and stop times ranging from a few seconds to several minutes are obtained.

[0104] Step 2: Treat the individuals in the crowd as jellyfish individuals and randomly initialize the position coordinates (x, y) to cover the entire shopping mall area. According to the statistical average of the collected crowd behavior data, determine the individual's horizontal and vertical velocity components (v x ,v y ) initial values, for example, the horizontal velocity is 0.8 m / s and the vertical velocity is 0.3 m / s.

[0105] Define the channel attraction parameter as The turning probability parameter of the node is p jk , the probability parameter for choosing stairs, escalators, and elevators is p s 、p l and p m .

[0106] In the shopping mall scenario, the width of the main channel is 4 meters, the maximum width of all channels is 5 meters, the signage completeness is 0.8, the maximum signage completeness is 1, α = 0.6, β = 0.4, then the channel attractiveness parameter is

[0107] Node turning probability parameter p jkAccording to historical data statistics, at a certain three-channel intersection, the probability of turning left is 0.35, the probability of going straight is 0.4, and the probability of turning right is 0.25;

[0108] The probability parameter p for choosing stairs, escalators, and elevators s 、p l and p m The probability of selecting an elevator near a popular store is higher and is initialized to 0.4, while the probability of selecting stairs in a relatively remote location is lower and is initialized to 0.15. The value range of each parameter is between [0, 1].

[0109] Step 3: Determine the population size N = 200 to fully simulate the diversity of people in a large shopping mall during peak hours. Other decision variables (channel attraction parameters Node turning probability parameter p jk , Stairs, escalators, elevators selection parameters p s 、p l and p m , speed adjustment parameter v adjust ) is initialized within the value range.

[0110] Step 4: Treat the various factors that guide the flow of people in the shopping center's traffic space as ocean currents, calculate the ocean current direction vectors of each influencing factor, and the jellyfish individuals move actively (attraction) and passively (repulsion) with the ocean currents.

[0111] For channel i, the ocean current direction vector is

[0112] Where, the vectors corresponding to the width and identification factors are And the corresponding weight coefficients α and β.

[0113] The ocean current direction vector of node j is Where, the turning probability parameter p for each turning direction k is jk , and the environment vector associated with each steering direction Here the turning probability parameter p jk As a weight, it reflects the probability of different turning directions in people's choices.

[0114] For stairs s, escalators l, and ladders m, the current direction vector

[0115] In the formula, the vectors corresponding to factors such as location and surrounding store attractiveness are and weight coefficients δ and ε, where δ+ε=1.

[0116] Each jellyfish individual is attracted by Includes channel attraction Node attractiveness Stairs / elevator or escalator / staircase attractiveness Target attractiveness

[0117] Among them, the channel attraction is: Among them, k1 is the channel attraction coefficient, is the channel attraction weight parameter of the channel where the individual is located, is the channel’s current direction vector;

[0118] The node attraction is: Among them, k2 is the node attraction coefficient, j represents the nodes around individual n, and p nj is the probability that individual n is affected by node j, is the current direction vector of the node;

[0119] Stairs / elevators or escalators / staircases have the following attractions: Where k3 is the attraction coefficient of stairs / elevators or escalators / elevators, s represents the stairs / elevators or escalators / elevators around individual n, is the probability that an individual chooses stairs / elevator or escalator / elevator, is the current direction vector of the stairs / elevator or escalator / staircase;

[0120] The target attraction is: Among them, k4 is the target attraction coefficient, is the vector pointing from the individual position to the target position, is the distance from the individual to the target location;

[0121] The total attraction is:

[0122] During the movement, the jellyfish individuals n will continue to move with the ocean currents and adjust their movement speed and direction.

[0123] For any two jellyfish individuals n and q, the repulsive force between the jellyfish individuals is For any two jellyfish individuals n and q, the repulsive force is proportional to the distance d between them. nq Inversely proportional, the repulsive force factor is r, then in is the vector pointing from individual n to individual q, and all the repulsive forces interacting with individual n are superimposed, and the total repulsive force is

[0124] During the prediction process, the speed and movement position of the jellyfish in continuous time are updated based on the time step Δt;

[0125] The speed update formula is: in, is the new velocity of individual n at time t+Δt, is the old velocity of individual n at time t, w is the inertia weight, a is the attraction factor, r is the repulsion factor, is the attraction experienced by individual n at time t, is the repulsive force experienced by individual n at time t;

[0126] The position update formula is: in, is the new position of individual n at time t+Δt, is the old position of individual n at time t.

[0127] During the update process, ensure that the parameters are within a reasonable range. For example, the channel attraction weight should be between 0 and 1, and the steering probability should also be between 0 and 1. If the parameter is out of range, adjust it to return it to a reasonable range.

[0128] Using the fitness function, F(v i )=w1E distance +w2E node +w3E flow .

[0129] Among them, w1, w2, w3 are weight coefficients, E distance is the trajectory distance error, E node is the node passing error, E flow The flow error measures how closely the predicted trajectories match actual crowd behavior. Trajectory distance error is calculated by calculating the average Euclidean distance between the predicted and actual trajectory points. Node passage error measures the difference between the predicted time it takes for people to pass through key nodes and the actual time it takes them to pass through. The flow error compares the difference between the predicted and actual flow rates for different elements of the traffic space.

[0130] The algorithm terminates when the maximum number of iterations is reached or when an individual in the population has a fitness value below a pre-set accuracy threshold. The number of consecutive iterations, M, is 200, and the threshold, ξ, is 0.03. During the iterations, the positional changes of all jellyfish are continuously monitored. When the termination condition is met, the iterations are stopped. The jellyfish trajectories over time are output, representing the predicted population trajectories.

[0131] The present invention is a method for predicting crowd trajectories in shopping mall traffic spaces based on an artificial jellyfish search algorithm. The artificial jellyfish search algorithm simulates the searching behavior of jellyfish in the ocean, including following ocean currents, moving within a group, and a time-controlled mechanism for switching movement types. This biomimetic characteristic enables the algorithm to better simulate the behavioral changes of people in complex traffic spaces when tackling the problem of crowd trajectory prediction in shopping malls. Crowd movement within a shopping mall is influenced by multiple factors, much like jellyfish are affected by factors such as ocean currents and food distribution. Within a shopping mall, people naturally flow along aisles (similar to ocean currents) and change direction or speed due to factors such as store distribution (similar to food sources) and signage (similar to clues in the ocean environment). The artificial jellyfish search algorithm naturally captures these behavioral characteristics, making predictions more consistent with actual crowd movement patterns. The artificial jellyfish search algorithm inherently possesses strong optimization capabilities and rapid convergence. When predicting crowd trajectories, it can quickly find the optimal solution, reducing computation time and resource consumption. Compared to some traditional trajectory prediction algorithms, it achieves superior prediction results without requiring extensive iterative calculations.

[0132] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for predicting crowd trajectories in the traffic space of a shopping mall based on the artificial jellyfish search algorithm.

[0133] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of the method for predicting crowd trajectories in a shopping mall traffic space based on an artificial jellyfish search algorithm.

[0134] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DRRAM). It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0135] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0136] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0137] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0138] The above is a detailed introduction to the method and system for predicting crowd trajectories in shopping center traffic spaces based on the artificial jellyfish search algorithm proposed in the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for predicting crowd trajectories in shopping mall traffic space based on an artificial jellyfish search algorithm, characterized by: The method comprises the following steps: Step 1: Collect shopping mall scene parameter information and crowd behavior data, establish a dedicated data fusion platform, and associate shopping mall scene parameter information and crowd behavior data so that the crowd behavior data can be mapped to specific scene parameters; Step 2: Treat individuals in the crowd as jellyfish individuals and determine their position coordinates ( x ,y ) is used as the basic decision variable, and the individual's velocity components in the horizontal and vertical directions are ( v x ,v y ), the initial value of the speed is determined according to the statistical average of the collected crowd behavior data; other decision variables are defined as channel attraction parameters , node turning probability parameter , Stairs, escalators, and elevator selection parameters p s 、 p l and p m And speed adjustment parameters v adjust ; Step 3: Initialize the jellyfish population and determine the population size N ; Initialization position coordinates of jellyfish individuals ( x ,y ) is randomly generated within the shopping mall, and the velocity component ( v x ,v y ) Randomly initialize according to the speed range of the collected data, and initialize other decision variables within the value range; Step 4: Use the artificial jellyfish search algorithm to iterate the algorithm to obtain the final crowd trajectory prediction; The specific prediction trajectory is as follows: various factors that guide the flow of people in the shopping center traffic space are regarded as ocean currents, and the channel is calculated separately. i ,node j ,stairs s , escalator l and vertical ladders m The ocean current direction vector; for each jellyfish individual n , calculate the effect of ocean currents on individual n The attraction and the repulsive force between individual jellyfish ; Determine the time step based on the characteristics of the data acquisition device Based on the calculated attraction, repulsion, and inertia weights, the speed and position of each individual in the crowd are updated to obtain new speed and position coordinates, thereby simulating the movement trajectory of the crowd in the traffic space. When updating positions, it is necessary to check whether individual crowd members exceed the boundaries of the shopping center. That is, the channel attraction parameters, node turning probability parameters, and stair, escalator, and elevator selection parameters are all within the interval [0, 1]. If they exceed the boundaries, the parameters need to be adjusted back to a reasonable range to ensure that the predicted trajectory meets the actual spatial constraints. Set the iteration termination condition, that is, all jellyfish individuals are in the continuous M The position change within iterations is less than a certain threshold ξ ,When the termination condition is met, the iteration stops and the output is the jellyfish trajectory in continuous time, i.e. the predicted crowd trajectory.

2. The method according to claim 1, characterized in that Establish a data fusion platform that links shopping mall scene parameter information and crowd behavior data. The data fusion platform adopts a three-layer architecture, including data collection layer, edge computing layer and cloud fusion analysis layer; Among them, the data collection layer is composed of Wi-Fi positioning devices and smart cameras, which are responsible for collecting scene parameter information and crowd behavior data of the shopping mall; the edge computing layer is distributed in various areas of the shopping mall, close to the data collection equipment, and is composed of multiple edge computing nodes. These nodes can perform real-time pre-processing and preliminary analysis of the collected data, filter out redundant information to obtain the required traffic space information and crowd behavior information; the cloud fusion analysis layer is located in the data center, which is used to deeply integrate, analyze and store data from the edge computing layer.

3. The method according to claim 1, characterized in that Channel attraction parameter , the node's turning probability parameter , the selection probability parameters of stairs, escalators, and elevators p s 、 p l and p m The value range of is [0, 1], and each parameter is affected by different factors; Among them, each channel i Channel attraction parameter Affected by: Channel width , completeness of identification The weights of each factor are α and β , and meet α + β =1, calculate ,in, 、 are the maximum values ​​of all channel widths and identification perfection respectively; Each node j Every possible direction of k The turning probability parameter , ; Each staircase s , escalator l and vertical ladders m , choose the probability parameter p s 、 p l and p m , are all affected by the convenience of location s / l / m Attraction of surrounding shops s / l / m Influence.

4. The method according to claim 1, wherein The various factors that guide the flow of people in the shopping center traffic space are regarded as ocean currents. Depending on the influencing factors, the ocean currents have different direction vectors. Among them, for the channel i , according to the vector corresponding to its width and identity factor 、 , and the corresponding weight coefficients α 、 β , the current direction vector of the channel is ; For nodes j , according to its various turning directions k The turning probability parameter , and the environment vector associated with each steering direction ,node j The ocean current direction vector is , turning probability parameter , which reflects the possibility of different turning directions in the crowd’s choice; For stairs s , escalator l and vertical ladders m , according to its location and the surrounding store attractiveness factors, the corresponding vector is 、 , and the weight coefficient δ and ε ,in δ + ε =1, calculate the ocean current direction vector .

5. The method according to claim 1, wherein Each jellyfish individual is attracted by , including channel attraction Node attractiveness , Stairs / elevators or escalators / staircases attractiveness Target attractiveness ; Among them, the channel attraction is: ,in, is the channel attraction coefficient, is the channel attraction weight parameter of the channel where the individual is located, is the channel’s current direction vector; The node attraction is: ,in, is the node attraction coefficient, j Represents an individual n The surrounding nodes, is an individual n Node j The probability of impact, is the current direction vector of the node; Stairs / elevators or escalators / staircases have the following attractions: ,in, is the attraction coefficient of stairs / elevators or escalators / ladders, s Represents an individual n Stairs / elevators or escalators / ladders around you, Is it an individual choice of stairs / elevator or escalator / elevator s The probability of Is it stairs / elevator or escalator / staircase s The ocean current direction vector; The target attraction is: ,in, is the target attraction coefficient, is the vector pointing from the individual position to the target position, is the distance from the individual to the target location; The total attraction is: .

6. The method according to claim 1, characterized in that For any two jellyfish individuals n and q , the repulsive force between jellyfish individuals is , for any two jellyfish individuals n and q , the repulsive force and the distance between them Inversely proportional, the repulsive force factor is r ,but ,in From the individual n Pointing to individuals q vector, which will be all the individual n The repulsive forces of each other are superimposed, and the total repulsive force is .

7. The method according to claim 1, characterized in that During the prediction process, based on the time step , update the speed and movement position of the jellyfish in continuous time; The speed update formula is: ,in, is an individual n At the moment The new speed, is an individual n exist t The old speed of time, is the inertia weight, is the attraction factor, is the repulsive force factor, is an individual n exist t The attraction that is always there, is an individual n exist t The repulsive force that is constantly being felt; The position update formula is: ,in, is an individual n exist The new position of the moment, is an individual n exist t The old position of the moment.

8. A shopping center traffic space crowd trajectory prediction system based on an artificial jellyfish search algorithm, characterized by: The system comprises: Data collection module: collects shopping mall scene parameter information and crowd behavior data, establishes a dedicated data fusion platform, and associates shopping mall scene parameter information and crowd behavior data so that crowd behavior data can be mapped to specific scene parameters; Decision variable setting module: treat individuals in the crowd as jellyfish individuals and determine the position coordinates ( x ,y ) is used as the basic decision variable, and the individual's velocity components in the horizontal and vertical directions are ( v x ,v y ), the initial value of the speed is determined according to the statistical average of the collected crowd behavior data; other decision variables are defined as channel attraction parameters , node turning probability parameter , Stairs, escalators, and elevator selection parameters p s 、 p l and p m And speed adjustment parameters v adjust ; Initialization module: initialize the jellyfish population and determine the population size N ; Initialization position coordinates of jellyfish individuals ( x ,y ) is randomly generated within the shopping mall, and the velocity component ( v x ,v y ) Randomly initialize according to the speed range of the collected data, and initialize other decision variables within the value range; Trajectory prediction module: uses the artificial jellyfish search algorithm to perform algorithm iteration to obtain the final crowd trajectory prediction; The prediction trajectory is specifically as follows: various factors that guide the flow of people in the shopping center traffic space are regarded as ocean currents, and the channel is calculated separately. i ,node j ,stairs s , escalator l and vertical ladders m The ocean current direction vector; for each jellyfish individual n , calculate the effect of ocean currents on individual n The attraction and the repulsive force between individual jellyfish ; Determine the time step based on the characteristics of the data acquisition device Based on the calculated attraction, repulsion, and inertia weights, the speed and position of each individual in the crowd are updated to obtain new speed and position coordinates, thereby simulating the movement trajectory of the crowd in the traffic space. When updating positions, it is necessary to check whether individual crowd members exceed the boundaries of the shopping center. That is, the channel attraction parameters, node turning probability parameters, and stair, escalator, and elevator selection parameters are all within the interval [0, 1]. If they exceed the boundaries, the parameters need to be adjusted back to a reasonable range to ensure that the predicted trajectory meets the actual spatial constraints. Set the iteration termination condition, that is, all jellyfish individuals are in the continuous M The position change within iterations is less than a certain threshold ξ ,When the termination condition is met, the iteration stops and the output is the jellyfish trajectory in continuous time, i.e. the predicted crowd trajectory.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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